Abstract
We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a new method to aggregate salient predictions from a set of cloze query templates and show that domain-adaptation using either a small, high-quality or a customized Wikipedia corpus boosts performance. When exploring resource-lean alternatives, we find a distilled PLM clearly outperforming a classic pattern-based algorithm. Further, given that 98% of our domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.- Anthology ID:
- 2023.eacl-main.104
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1420–1435
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.104
- DOI:
- 10.18653/v1/2023.eacl-main.104
- Cite (ACL):
- Annerose Eichel, Helena Schlipf, and Sabine Schulte im Walde. 2023. Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1420–1435, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain (Eichel et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.eacl-main.104.pdf